Enhanced 'build_apk.py' logic & 'README.md' (#41)

   Enhanced 'build_apk.py' logic & 'README.md'.

There was an issue in 'build_apk.py': when only 'qnnsdk' and
'model_path' were provided as inputs, it incorrectly invoked the
'printmenu()' function to display a model selection menu, despite the
model always being renamed to 'classification.tflite'. To address this,
I adjusted the logic so that the model selection menu is only printed
when neither 'model_name' nor 'model_path' is specified by the user.
Specifically, I changed line 45 of 'build_apk.py' to read:
    ```
    if not args.model_name and not args.model_path:
    ```
    
Additionally, I made several descriptive improvements to the 'README.md'
file. Recognizing that newcomers primarily aim to compile and install
the ImageClassification application, I included more concise and
efficient instructions tailored to their needs, aiming to minimize their
trial-and-error process and facilitate a smoother onboarding
experience."

apps/android/ImageClassification/README.md

@@ -2,6 +2,10 @@
 
 1. Java, android-sdk and sdkmanager is already set at user's end
 2. User should have Linux QNN SDK in local machine.
+3. If models need to be downloaded during the execution of build_apk.py, AI Hub Models must be installed first according to the [README.md in ai-hub-models](https://github.com/quic/ai-hub-models/blob/main/README.md).
+
+4. When downloading [MobileNet_v3_Small](https://aihub.qualcomm.com/mobile/models/mobilenet_v3_small) via the web interface, AI Hub Models installation can be temporarily skipped.
+
 
 
 ## Info
@@ -53,20 +57,33 @@ Right now we use mobilenet_v3_small.tflite model which takes 224x224 as input an
 
 You have to run build_apk.py for Image Classification. It will generate classification-debug.apk and install it in connected device.
 
+Please first use `python build_apk.py -h` to understand the parameters.
+
+
+```
+build_apk.py [-h] -q QNNSDK [-m MODEL_NAME] [-path MODEL_PATH]
+```
 
-    build_apk.py [-h] -q QNNSDK (-m MODEL_PATH | -e MODEL_NAME)
+The parameter "-q" must be entered. QNN SDK can be utilized by setting an environment variable or specifying the path directly.<br />There are three ways to input parameters:<br />1. Inputting only the parameter "-path"<br />2. Inputting only the parameter "-m"<br />3. Neither entering the parameter "-m" nor "-path"
 
 
 
 ### Example
 
-Here, with -m, give your tflite model path i.e. till `*.tflite file`, and it will copy model file to assets folder to build andoid app.
+Here, with -path, give your tflite model path i.e. till `*.tflite file`, and it will copy model file to assets folder to build andoid app.
 ```
-    python build_apk.py -q "<QNN_SDK_PATH>" -m "Path\to\TFLITE\Model"
+    python build_apk.py -q "<QNN_SDK_PATH>" -path "Path/to/TFLITE/Model"
 ```
 
+
 Also, you can use AI-HUB Model name as mentioned in models directory, to directly export the model from AI-Hub and copy it to app assets.
 
 ```
-    python build_apk.py -q "<QNN_SDK_PATH>" -e <Model Name>
+    python build_apk.py -q "<QNN_SDK_PATH>" -m <Model Name>
+```
+
+You can also select the model provided in the list menu during the execution of build_apk.py without specifying the model name and model path.
+
+```
+    python build_apk.py -q "<QNN_SDK_PATH>" 
 ```

apps/android/ImageClassification/build_apk.py

@@ -42,7 +42,7 @@ def printmenu():
 
 
 ##based on this pre-post can be decided
-if not args.model_name:
+if not args.model_name and not args.model_path:
     printmenu()
     inp_model_name = int(input("Please select one: "))
     args.model_name = MODELNAME(inp_model_name).name